Numerical Experiments for Bayesian Illumination This file contains the numerical experiments conducted for the manuscript titled “Bayesian Illumination: Inference and Quality-Diversity Accelerate Generative Molecular Models.” The experiments provide comprehensive benchmarking and validation of the Bayesian Illumination algorithm, which integrates Bayesian optimization with quality-diversity methods to improve molecular discovery. The data includes: Descriptor-Based Rediscovery: Results from a novel benchmark where molecules are rediscovered based on conformer samples and descriptor-based representations (USRCAT and Zernike descriptors). Efficient Organic Photovoltaics: Performance metrics from multiple tasks, including maximising HOMO-LUMO gap values, minimising LUMO energyvalues, maximising the molecular dipole moment and maximising a combined efficiency score). Docking-Based Tasks: Outputs from docking-based optimizations, including stringent structural and physicochemical filtering to ensure realistic molecular designs. This also includes synthetic accessibility-adjusted docking scores. This file serves as a supplement to the manuscript, providing the raw data and detailed performance metrics to support the reproducibility of the findings.